Entity

Time filter

Source Type


Zhou Q.,CAS Shanghai Institute of Microsystem and Information Technology | Zhou Q.,Science and Technology on Micro System Laboratory | Zhou Q.,University of Chinese Academy of Sciences | Tong G.,CAS Shanghai Institute of Microsystem and Information Technology | And 5 more authors.
IEEE Transactions on Magnetics | Year: 2012

The direction of a moving target is an important piece of information in many wireless sensor network (WSN) applications, such as in boundary security, traffic flow control, etc. Due to its robustness, the magnetic sensor can be used to detect a passing ferromagnetic object. By using the orthogonality of two perpendicularly placed sensing units in a monomer magnetic sensor, a linear algorithm based on a magnetic dipole model to identify the ferromagnetic object's moving direction is introduced in this paper. It has been successfully applied in real WSN applications to reduce the numbers of nodes, and prolong the lifetime of the network. Both simulation and field experiments show it has strong noise immunity and more than 95% correction rate in direction detecting. © 2012 IEEE. Source


Zhou Q.,CAS Shanghai Institute of Microsystem and Information Technology | Zhou Q.,Science and Technology on Micro System Laboratory | Zhou Q.,University of Chinese Academy of Sciences | Li B.,CAS Shanghai Institute of Microsystem and Information Technology | And 11 more authors.
Journal of Sound and Vibration | Year: 2013

Seismic signals are widely used in ground vehicle classification due to their inherent characteristics. In this paper, a kind of feature extracted by the Short-time Power Spectrum Density (STPSD) from ground-vehicle-induced seismic signals was introduced for wheeled and tracked vehicle distinction. According to theoretical analysis, based on a simplified quarter-car vehicle model, the cepstrums of the STPSD of seismic signals can depict the structure differences of wheel and track. In addition, the extracted feature is also less affected by the underlying geologies than traditional Power Spectrum Density based features. It is verified with mixed datasets from our field experiments and SensIT project. © 2013 Elsevier Ltd. Source


Zhou Q.,Science and Technology on Micro System Laboratory | Zhou Q.,Shanghai Institute of Technology | Zhou Q.,University of Chinese Academy of Sciences | Li B.,Science and Technology on Micro System Laboratory | And 7 more authors.
Proceedings - 2013 International Conference on Computational and Information Sciences, ICCIS 2013 | Year: 2013

Seismic signal is widely used in ground vehicle classification due to its inherent characteristics. But the generalization accuracy of classifier is heavily degraded due to different underlying geologies. To overcome the weakness of the seismic signal, a feature extraction method is proposed in this paper. The extracted feature is the cepstrum of the seismic signal whose logarithmic power spectrum density will be preprocessed to suppress the geology related components, which is based on the special characteristics of the employed geologic model, before further calculations. The efficiency of the proposed feature is verified with a mixed database taking from our field experiments and SensIT project. © 2013 IEEE. Source


Huang J.,Science and Technology on Micro System Laboratory | Huang J.,Shanghai Institute of Technology | Huang J.,University of Chinese Academy of Sciences | Zhang X.,Science and Technology on Micro System Laboratory | And 9 more authors.
IEEE Transactions on Instrumentation and Measurement | Year: 2014

In this paper, a practical method is proposed for a moving target's fundamental frequency (MTFF) extraction from its acoustic signal. This method is developed for the application of motion parameters estimation. Starting from the analysis of the target frequency model and the acoustic Doppler model, the characteristics of moving target's signal are discussed. Based on the signatures of target's acoustic signal, a new approximate greatest common divisor (AGCD) method is developed to obtain an initial fundamental frequency (IFF). Then, the corresponding harmonic number associated with the IFF is determined by maximizing an objective function formulated as an impulse-train-weighted symmetric average magnitude sum function (SAMSF) of the observed signal. The frequency of the SAMSF is determined by target's acoustic signal, the period of the impulse train is controlled by the estimated IFF harmonic, and the maximization of the objective function is carried out through a time-domain matching of periodicity of the impulse train with that of the SAMSF. Finally, a precise fundamental frequency is achieved based on the obtained IFF and its harmonic number. In order to demonstrate the effectiveness of the proposed method, experiments are conducted on wheeled vehicles, tracked vehicles, and propeller-driven aircrafts. Evaluation of the algorithm performance in comparison with other traditional methods indicates that the proposed MTFF is practical for the fundamental frequency extraction of moving targets. © 2013 IEEE. Source


Huang J.,Science and Technology on Micro System Laboratory | Huang J.,Chinese Academy of Sciences | Huang J.,University of Chinese Academy of Sciences | Zhou Q.,Science and Technology on Micro System Laboratory | And 11 more authors.
Sensors (Switzerland) | Year: 2013

One of the most challenging problems in target classification is the extraction of a robust feature, which can effectively represent a specific type of targets. The use of seismic signals in unattended ground sensor (UGS) systems makes this problem more complicated, because the seismic target signal is non-stationary, geology-dependent and with high-dimensional feature space. This paper proposes a new feature extraction algorithm, called wavelet packet manifold (WPM), by addressing the neighborhood preserving embedding (NPE) algorithm of manifold learning on the wavelet packet node energy (WPNE) of seismic signals. By combining non-stationary information and low-dimensional manifold information, WPM provides a more robust representation for seismic target classification. By using a K nearest neighbors classifier on the WPM signature, the algorithm of wavelet packet manifold classification (WPMC) is proposed. Experimental results show that the proposed WPMC can not only reduce feature dimensionality, but also improve the classification accuracy up to 95.03%. Moreover, compared with state-of-the-art methods, WPMC is more suitable for UGS in terms of recognition ratio and computational complexity. © 2013 by the authors; licensee MDPI, Basel, Switzerland. Source

Discover hidden collaborations